Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.3 MiB
Average record size in memory453.0 B

Variable types

Numeric8
Categorical8

Alerts

Caffeine_mg is highly overall correlated with Coffee_IntakeHigh correlation
Coffee_Intake is highly overall correlated with Caffeine_mgHigh correlation
Health_Issues is highly overall correlated with Stress_LevelHigh correlation
Sleep_Hours is highly overall correlated with Sleep_Quality and 1 other fieldsHigh correlation
Sleep_Quality is highly overall correlated with Sleep_Hours and 1 other fieldsHigh correlation
Stress_Level is highly overall correlated with Health_Issues and 2 other fieldsHigh correlation
ID is uniformly distributed Uniform
ID has unique values Unique
Coffee_Intake has 558 (5.6%) zeros Zeros
Caffeine_mg has 528 (5.3%) zeros Zeros

Reproduction

Analysis started2025-09-17 15:09:10.114427
Analysis finished2025-09-17 15:09:22.515313
Duration12.4 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

ID
Real number (ℝ)

Uniform  Unique 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5000.5
Minimum1
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-09-17T15:09:22.694700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile500.95
Q12500.75
median5000.5
Q37500.25
95-th percentile9500.05
Maximum10000
Range9999
Interquartile range (IQR)4999.5

Descriptive statistics

Standard deviation2886.8957
Coefficient of variation (CV)0.5773214
Kurtosis-1.2
Mean5000.5
Median Absolute Deviation (MAD)2500
Skewness0
Sum50005000
Variance8334166.7
MonotonicityStrictly increasing
2025-09-17T15:09:22.912805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
6671 1
 
< 0.1%
6664 1
 
< 0.1%
6665 1
 
< 0.1%
6666 1
 
< 0.1%
6667 1
 
< 0.1%
6668 1
 
< 0.1%
6669 1
 
< 0.1%
6670 1
 
< 0.1%
6672 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
10000 1
< 0.1%
9999 1
< 0.1%
9998 1
< 0.1%
9997 1
< 0.1%
9996 1
< 0.1%
9995 1
< 0.1%
9994 1
< 0.1%
9993 1
< 0.1%
9992 1
< 0.1%
9991 1
< 0.1%

Age
Real number (ℝ)

Distinct59
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.9491
Minimum18
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-09-17T15:09:23.121571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile18
Q126
median34
Q343
95-th percentile54
Maximum80
Range62
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.160939
Coefficient of variation (CV)0.3193484
Kurtosis-0.34571983
Mean34.9491
Median Absolute Deviation (MAD)8
Skewness0.36185229
Sum349491
Variance124.56657
MonotonicityNot monotonic
2025-09-17T15:09:23.333225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 935
 
9.3%
34 354
 
3.5%
32 352
 
3.5%
37 346
 
3.5%
35 338
 
3.4%
38 325
 
3.2%
30 310
 
3.1%
31 310
 
3.1%
36 308
 
3.1%
40 308
 
3.1%
Other values (49) 6114
61.1%
ValueCountFrequency (%)
18 935
9.3%
19 133
 
1.3%
20 154
 
1.5%
21 171
 
1.7%
22 194
 
1.9%
23 221
 
2.2%
24 229
 
2.3%
25 256
 
2.6%
26 225
 
2.2%
27 291
 
2.9%
ValueCountFrequency (%)
80 2
 
< 0.1%
77 1
 
< 0.1%
75 1
 
< 0.1%
73 2
 
< 0.1%
72 7
0.1%
71 2
 
< 0.1%
70 8
0.1%
69 2
 
< 0.1%
68 8
0.1%
67 8
0.1%

Gender
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size605.8 KiB
Female
5001 
Male
4773 
Other
 
226

Length

Max length6
Median length6
Mean length5.0228
Min length4

Characters and Unicode

Total characters50228
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowFemale

Common Values

ValueCountFrequency (%)
Female 5001
50.0%
Male 4773
47.7%
Other 226
 
2.3%

Length

2025-09-17T15:09:23.583459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-17T15:09:23.773711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
female 5001
50.0%
male 4773
47.7%
other 226
 
2.3%

Most occurring characters

ValueCountFrequency (%)
e 15001
29.9%
a 9774
19.5%
l 9774
19.5%
F 5001
 
10.0%
m 5001
 
10.0%
M 4773
 
9.5%
O 226
 
0.4%
t 226
 
0.4%
h 226
 
0.4%
r 226
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50228
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 15001
29.9%
a 9774
19.5%
l 9774
19.5%
F 5001
 
10.0%
m 5001
 
10.0%
M 4773
 
9.5%
O 226
 
0.4%
t 226
 
0.4%
h 226
 
0.4%
r 226
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50228
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 15001
29.9%
a 9774
19.5%
l 9774
19.5%
F 5001
 
10.0%
m 5001
 
10.0%
M 4773
 
9.5%
O 226
 
0.4%
t 226
 
0.4%
h 226
 
0.4%
r 226
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50228
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 15001
29.9%
a 9774
19.5%
l 9774
19.5%
F 5001
 
10.0%
m 5001
 
10.0%
M 4773
 
9.5%
O 226
 
0.4%
t 226
 
0.4%
h 226
 
0.4%
r 226
 
0.4%

Country
Categorical

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size619.9 KiB
Canada
 
543
India
 
524
Norway
 
523
China
 
521
UK
 
519
Other values (15)
7370 

Length

Max length11
Median length9
Mean length6.4596
Min length2

Characters and Unicode

Total characters64596
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGermany
2nd rowGermany
3rd rowBrazil
4th rowGermany
5th rowSpain

Common Values

ValueCountFrequency (%)
Canada 543
 
5.4%
India 524
 
5.2%
Norway 523
 
5.2%
China 521
 
5.2%
UK 519
 
5.2%
Sweden 513
 
5.1%
South Korea 512
 
5.1%
Finland 510
 
5.1%
Italy 509
 
5.1%
Switzerland 500
 
5.0%
Other values (10) 4826
48.3%

Length

2025-09-17T15:09:23.977109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
canada 543
 
5.2%
india 524
 
5.0%
norway 523
 
5.0%
china 521
 
5.0%
uk 519
 
4.9%
sweden 513
 
4.9%
south 512
 
4.9%
korea 512
 
4.9%
finland 510
 
4.9%
italy 509
 
4.8%
Other values (11) 5326
50.7%

Most occurring characters

ValueCountFrequency (%)
a 9592
14.8%
n 6066
 
9.4%
e 5002
 
7.7%
i 4474
 
6.9%
r 3978
 
6.2%
l 3463
 
5.4%
d 3084
 
4.8%
t 2512
 
3.9%
S 2459
 
3.8%
o 2030
 
3.1%
Other values (23) 21936
34.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 64596
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 9592
14.8%
n 6066
 
9.4%
e 5002
 
7.7%
i 4474
 
6.9%
r 3978
 
6.2%
l 3463
 
5.4%
d 3084
 
4.8%
t 2512
 
3.9%
S 2459
 
3.8%
o 2030
 
3.1%
Other values (23) 21936
34.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 64596
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 9592
14.8%
n 6066
 
9.4%
e 5002
 
7.7%
i 4474
 
6.9%
r 3978
 
6.2%
l 3463
 
5.4%
d 3084
 
4.8%
t 2512
 
3.9%
S 2459
 
3.8%
o 2030
 
3.1%
Other values (23) 21936
34.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 64596
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 9592
14.8%
n 6066
 
9.4%
e 5002
 
7.7%
i 4474
 
6.9%
r 3978
 
6.2%
l 3463
 
5.4%
d 3084
 
4.8%
t 2512
 
3.9%
S 2459
 
3.8%
o 2030
 
3.1%
Other values (23) 21936
34.0%

Coffee_Intake
Real number (ℝ)

High correlation  Zeros 

Distinct78
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.50923
Minimum0
Maximum8.2
Zeros558
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-09-17T15:09:24.188261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.5
median2.5
Q33.5
95-th percentile5
Maximum8.2
Range8.2
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4502476
Coefficient of variation (CV)0.5779652
Kurtosis-0.27653965
Mean2.50923
Median Absolute Deviation (MAD)1
Skewness0.26336306
Sum25092.3
Variance2.1032181
MonotonicityNot monotonic
2025-09-17T15:09:24.398151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 558
 
5.6%
2.7 305
 
3.0%
2.8 270
 
2.7%
2.1 269
 
2.7%
2.2 266
 
2.7%
2.5 264
 
2.6%
2.6 264
 
2.6%
2.4 258
 
2.6%
3.1 255
 
2.5%
2.9 249
 
2.5%
Other values (68) 7042
70.4%
ValueCountFrequency (%)
0 558
5.6%
0.1 64
 
0.6%
0.2 81
 
0.8%
0.3 88
 
0.9%
0.4 102
 
1.0%
0.5 135
 
1.4%
0.6 109
 
1.1%
0.7 124
 
1.2%
0.8 129
 
1.3%
0.9 142
 
1.4%
ValueCountFrequency (%)
8.2 1
 
< 0.1%
7.8 2
< 0.1%
7.7 2
< 0.1%
7.6 3
< 0.1%
7.3 4
< 0.1%
7.2 1
 
< 0.1%
7.1 1
 
< 0.1%
7 4
< 0.1%
6.9 3
< 0.1%
6.8 3
< 0.1%

Caffeine_mg
Real number (ℝ)

High correlation  Zeros 

Distinct4277
Distinct (%)42.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean238.41101
Minimum0
Maximum780.3
Zeros528
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-09-17T15:09:24.608218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1138.75
median235.4
Q3332.025
95-th percentile474.005
Maximum780.3
Range780.3
Interquartile range (IQR)193.275

Descriptive statistics

Standard deviation137.74881
Coefficient of variation (CV)0.57777875
Kurtosis-0.27574097
Mean238.41101
Median Absolute Deviation (MAD)96.65
Skewness0.26318051
Sum2384110.1
Variance18974.736
MonotonicityNot monotonic
2025-09-17T15:09:24.842865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 528
 
5.3%
148.1 9
 
0.1%
323.3 9
 
0.1%
259.3 9
 
0.1%
265.3 9
 
0.1%
132.1 9
 
0.1%
277.9 9
 
0.1%
274.9 8
 
0.1%
191.3 8
 
0.1%
265.9 8
 
0.1%
Other values (4267) 9394
93.9%
ValueCountFrequency (%)
0 528
5.3%
0.1 1
 
< 0.1%
0.3 2
 
< 0.1%
0.4 2
 
< 0.1%
0.7 1
 
< 0.1%
0.8 1
 
< 0.1%
0.9 2
 
< 0.1%
1.3 1
 
< 0.1%
1.5 2
 
< 0.1%
1.6 1
 
< 0.1%
ValueCountFrequency (%)
780.3 1
< 0.1%
742.2 1
< 0.1%
738.8 1
< 0.1%
733.1 1
< 0.1%
729.2 1
< 0.1%
723.9 1
< 0.1%
722.4 1
< 0.1%
720 1
< 0.1%
693.8 1
< 0.1%
693.4 1
< 0.1%

Sleep_Hours
Real number (ℝ)

High correlation 

Distinct71
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.63622
Minimum3
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-09-17T15:09:25.090293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4.6
Q15.8
median6.6
Q37.5
95-th percentile8.6
Maximum10
Range7
Interquartile range (IQR)1.7

Descriptive statistics

Standard deviation1.2220554
Coefficient of variation (CV)0.18414932
Kurtosis-0.13774913
Mean6.63622
Median Absolute Deviation (MAD)0.8
Skewness-0.018317325
Sum66362.2
Variance1.4934195
MonotonicityNot monotonic
2025-09-17T15:09:25.321467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.7 333
 
3.3%
6.9 331
 
3.3%
6.6 324
 
3.2%
7.1 323
 
3.2%
6.5 320
 
3.2%
6.8 313
 
3.1%
6.4 312
 
3.1%
6.1 311
 
3.1%
7 306
 
3.1%
6.2 304
 
3.0%
Other values (61) 6823
68.2%
ValueCountFrequency (%)
3 14
0.1%
3.1 6
 
0.1%
3.2 6
 
0.1%
3.3 9
 
0.1%
3.4 7
 
0.1%
3.5 9
 
0.1%
3.6 12
0.1%
3.7 17
0.2%
3.8 25
0.2%
3.9 25
0.2%
ValueCountFrequency (%)
10 40
0.4%
9.9 8
 
0.1%
9.8 14
 
0.1%
9.7 5
 
0.1%
9.6 21
0.2%
9.5 22
0.2%
9.4 23
0.2%
9.3 26
0.3%
9.2 37
0.4%
9.1 38
0.4%

Sleep_Quality
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size602.4 KiB
Good
5637 
Fair
2050 
Excellent
1352 
Poor
961 

Length

Max length9
Median length4
Mean length4.676
Min length4

Characters and Unicode

Total characters46760
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGood
2nd rowGood
3rd rowFair
4th rowGood
5th rowFair

Common Values

ValueCountFrequency (%)
Good 5637
56.4%
Fair 2050
 
20.5%
Excellent 1352
 
13.5%
Poor 961
 
9.6%

Length

2025-09-17T15:09:25.539579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-17T15:09:25.699905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
good 5637
56.4%
fair 2050
 
20.5%
excellent 1352
 
13.5%
poor 961
 
9.6%

Most occurring characters

ValueCountFrequency (%)
o 13196
28.2%
G 5637
12.1%
d 5637
12.1%
r 3011
 
6.4%
e 2704
 
5.8%
l 2704
 
5.8%
F 2050
 
4.4%
a 2050
 
4.4%
i 2050
 
4.4%
E 1352
 
2.9%
Other values (5) 6369
13.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46760
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 13196
28.2%
G 5637
12.1%
d 5637
12.1%
r 3011
 
6.4%
e 2704
 
5.8%
l 2704
 
5.8%
F 2050
 
4.4%
a 2050
 
4.4%
i 2050
 
4.4%
E 1352
 
2.9%
Other values (5) 6369
13.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46760
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 13196
28.2%
G 5637
12.1%
d 5637
12.1%
r 3011
 
6.4%
e 2704
 
5.8%
l 2704
 
5.8%
F 2050
 
4.4%
a 2050
 
4.4%
i 2050
 
4.4%
E 1352
 
2.9%
Other values (5) 6369
13.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46760
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 13196
28.2%
G 5637
12.1%
d 5637
12.1%
r 3011
 
6.4%
e 2704
 
5.8%
l 2704
 
5.8%
F 2050
 
4.4%
a 2050
 
4.4%
i 2050
 
4.4%
E 1352
 
2.9%
Other values (5) 6369
13.6%

BMI
Real number (ℝ)

Distinct220
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.98686
Minimum15
Maximum38.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-09-17T15:09:25.903533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile17.4
Q121.3
median24
Q326.6
95-th percentile30.3
Maximum38.2
Range23.2
Interquartile range (IQR)5.3

Descriptive statistics

Standard deviation3.9064113
Coefficient of variation (CV)0.1628563
Kurtosis-0.16490502
Mean23.98686
Median Absolute Deviation (MAD)2.7
Skewness0.047970803
Sum239868.6
Variance15.260049
MonotonicityNot monotonic
2025-09-17T15:09:26.118999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 120
 
1.2%
25.5 119
 
1.2%
23.2 116
 
1.2%
24 115
 
1.1%
24.7 111
 
1.1%
23.3 111
 
1.1%
25.4 110
 
1.1%
24.9 108
 
1.1%
23.7 107
 
1.1%
25 106
 
1.1%
Other values (210) 8877
88.8%
ValueCountFrequency (%)
15 120
1.2%
15.1 9
 
0.1%
15.2 6
 
0.1%
15.3 11
 
0.1%
15.4 16
 
0.2%
15.5 8
 
0.1%
15.6 20
 
0.2%
15.7 13
 
0.1%
15.8 11
 
0.1%
15.9 14
 
0.1%
ValueCountFrequency (%)
38.2 1
< 0.1%
37.9 1
< 0.1%
37.8 1
< 0.1%
37.5 1
< 0.1%
37.2 2
< 0.1%
37 1
< 0.1%
36.9 1
< 0.1%
36.8 1
< 0.1%
36.7 1
< 0.1%
36.6 1
< 0.1%

Heart_Rate
Real number (ℝ)

Distinct58
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.6178
Minimum50
Maximum109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-09-17T15:09:26.320885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile54
Q164
median71
Q377
95-th percentile87
Maximum109
Range59
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.8229513
Coefficient of variation (CV)0.13910022
Kurtosis-0.25748864
Mean70.6178
Median Absolute Deviation (MAD)7
Skewness0.10386586
Sum706178
Variance96.490372
MonotonicityNot monotonic
2025-09-17T15:09:26.555706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 433
 
4.3%
71 408
 
4.1%
73 392
 
3.9%
67 387
 
3.9%
72 386
 
3.9%
69 385
 
3.9%
74 384
 
3.8%
75 360
 
3.6%
66 358
 
3.6%
68 354
 
3.5%
Other values (48) 6153
61.5%
ValueCountFrequency (%)
50 214
2.1%
51 62
 
0.6%
52 87
 
0.9%
53 74
 
0.7%
54 96
1.0%
55 118
1.2%
56 161
1.6%
57 156
1.6%
58 188
1.9%
59 227
2.3%
ValueCountFrequency (%)
109 1
 
< 0.1%
107 1
 
< 0.1%
106 2
 
< 0.1%
104 1
 
< 0.1%
103 1
 
< 0.1%
102 2
 
< 0.1%
101 2
 
< 0.1%
100 6
0.1%
99 7
0.1%
98 11
0.1%

Stress_Level
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size593.0 KiB
Low
6989 
Medium
2050 
High
961 

Length

Max length6
Median length3
Mean length3.7111
Min length3

Characters and Unicode

Total characters37111
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLow
2nd rowLow
3rd rowMedium
4th rowLow
5th rowMedium

Common Values

ValueCountFrequency (%)
Low 6989
69.9%
Medium 2050
 
20.5%
High 961
 
9.6%

Length

2025-09-17T15:09:26.806304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-17T15:09:26.986912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
low 6989
69.9%
medium 2050
 
20.5%
high 961
 
9.6%

Most occurring characters

ValueCountFrequency (%)
L 6989
18.8%
o 6989
18.8%
w 6989
18.8%
i 3011
8.1%
M 2050
 
5.5%
e 2050
 
5.5%
d 2050
 
5.5%
u 2050
 
5.5%
m 2050
 
5.5%
H 961
 
2.6%
Other values (2) 1922
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37111
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 6989
18.8%
o 6989
18.8%
w 6989
18.8%
i 3011
8.1%
M 2050
 
5.5%
e 2050
 
5.5%
d 2050
 
5.5%
u 2050
 
5.5%
m 2050
 
5.5%
H 961
 
2.6%
Other values (2) 1922
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37111
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 6989
18.8%
o 6989
18.8%
w 6989
18.8%
i 3011
8.1%
M 2050
 
5.5%
e 2050
 
5.5%
d 2050
 
5.5%
u 2050
 
5.5%
m 2050
 
5.5%
H 961
 
2.6%
Other values (2) 1922
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37111
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 6989
18.8%
o 6989
18.8%
w 6989
18.8%
i 3011
8.1%
M 2050
 
5.5%
e 2050
 
5.5%
d 2050
 
5.5%
u 2050
 
5.5%
m 2050
 
5.5%
H 961
 
2.6%
Other values (2) 1922
 
5.2%

Physical_Activity_Hours
Real number (ℝ)

Distinct151
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.48704
Minimum0
Maximum15
Zeros27
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-09-17T15:09:27.185306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.8
Q13.7
median7.5
Q311.2
95-th percentile14.3
Maximum15
Range15
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation4.31518
Coefficient of variation (CV)0.57635327
Kurtosis-1.1976038
Mean7.48704
Median Absolute Deviation (MAD)3.7
Skewness0.0036676275
Sum74870.4
Variance18.620778
MonotonicityNot monotonic
2025-09-17T15:09:27.402255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.4 94
 
0.9%
3.7 88
 
0.9%
14.6 83
 
0.8%
9.5 82
 
0.8%
10.9 81
 
0.8%
11.2 81
 
0.8%
4.1 80
 
0.8%
1 79
 
0.8%
10.2 79
 
0.8%
7 78
 
0.8%
Other values (141) 9175
91.8%
ValueCountFrequency (%)
0 27
 
0.3%
0.1 55
0.5%
0.2 63
0.6%
0.3 71
0.7%
0.4 67
0.7%
0.5 63
0.6%
0.6 61
0.6%
0.7 78
0.8%
0.8 63
0.6%
0.9 70
0.7%
ValueCountFrequency (%)
15 36
0.4%
14.9 66
0.7%
14.8 57
0.6%
14.7 66
0.7%
14.6 83
0.8%
14.5 62
0.6%
14.4 73
0.7%
14.3 62
0.6%
14.2 52
0.5%
14.1 61
0.6%

Health_Issues
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size597.7 KiB
None
5941 
Mild
3579 
Moderate
 
463
Severe
 
17

Length

Max length8
Median length4
Mean length4.1886
Min length4

Characters and Unicode

Total characters41886
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone
2nd rowNone
3rd rowMild
4th rowMild
5th rowMild

Common Values

ValueCountFrequency (%)
None 5941
59.4%
Mild 3579
35.8%
Moderate 463
 
4.6%
Severe 17
 
0.2%

Length

2025-09-17T15:09:27.624728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-17T15:09:27.791229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
none 5941
59.4%
mild 3579
35.8%
moderate 463
 
4.6%
severe 17
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e 6918
16.5%
o 6404
15.3%
N 5941
14.2%
n 5941
14.2%
M 4042
9.7%
d 4042
9.7%
i 3579
8.5%
l 3579
8.5%
r 480
 
1.1%
a 463
 
1.1%
Other values (3) 497
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41886
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 6918
16.5%
o 6404
15.3%
N 5941
14.2%
n 5941
14.2%
M 4042
9.7%
d 4042
9.7%
i 3579
8.5%
l 3579
8.5%
r 480
 
1.1%
a 463
 
1.1%
Other values (3) 497
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41886
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 6918
16.5%
o 6404
15.3%
N 5941
14.2%
n 5941
14.2%
M 4042
9.7%
d 4042
9.7%
i 3579
8.5%
l 3579
8.5%
r 480
 
1.1%
a 463
 
1.1%
Other values (3) 497
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41886
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 6918
16.5%
o 6404
15.3%
N 5941
14.2%
n 5941
14.2%
M 4042
9.7%
d 4042
9.7%
i 3579
8.5%
l 3579
8.5%
r 480
 
1.1%
a 463
 
1.1%
Other values (3) 497
 
1.2%

Occupation
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size624.9 KiB
Office
2073 
Other
2038 
Student
1968 
Healthcare
1964 
Service
1957 

Length

Max length10
Median length7
Mean length6.9743
Min length5

Characters and Unicode

Total characters69743
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOther
2nd rowService
3rd rowOffice
4th rowOther
5th rowStudent

Common Values

ValueCountFrequency (%)
Office 2073
20.7%
Other 2038
20.4%
Student 1968
19.7%
Healthcare 1964
19.6%
Service 1957
19.6%

Length

2025-09-17T15:09:27.967705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-17T15:09:28.475583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
office 2073
20.7%
other 2038
20.4%
student 1968
19.7%
healthcare 1964
19.6%
service 1957
19.6%

Most occurring characters

ValueCountFrequency (%)
e 13921
20.0%
t 7938
11.4%
c 5994
8.6%
r 5959
8.5%
f 4146
 
5.9%
O 4111
 
5.9%
i 4030
 
5.8%
h 4002
 
5.7%
a 3928
 
5.6%
S 3925
 
5.6%
Other values (6) 11789
16.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 69743
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 13921
20.0%
t 7938
11.4%
c 5994
8.6%
r 5959
8.5%
f 4146
 
5.9%
O 4111
 
5.9%
i 4030
 
5.8%
h 4002
 
5.7%
a 3928
 
5.6%
S 3925
 
5.6%
Other values (6) 11789
16.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 69743
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 13921
20.0%
t 7938
11.4%
c 5994
8.6%
r 5959
8.5%
f 4146
 
5.9%
O 4111
 
5.9%
i 4030
 
5.8%
h 4002
 
5.7%
a 3928
 
5.6%
S 3925
 
5.6%
Other values (6) 11789
16.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 69743
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 13921
20.0%
t 7938
11.4%
c 5994
8.6%
r 5959
8.5%
f 4146
 
5.9%
O 4111
 
5.9%
i 4030
 
5.8%
h 4002
 
5.7%
a 3928
 
5.6%
S 3925
 
5.6%
Other values (6) 11789
16.9%

Smoking
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size566.5 KiB
0
7996 
1
2004 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7996
80.0%
1 2004
 
20.0%

Length

2025-09-17T15:09:28.669289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-17T15:09:28.812901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 7996
80.0%
1 2004
 
20.0%

Most occurring characters

ValueCountFrequency (%)
0 7996
80.0%
1 2004
 
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7996
80.0%
1 2004
 
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7996
80.0%
1 2004
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7996
80.0%
1 2004
 
20.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size566.5 KiB
0
6993 
1
3007 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 6993
69.9%
1 3007
30.1%

Length

2025-09-17T15:09:28.967952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-17T15:09:29.107151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 6993
69.9%
1 3007
30.1%

Most occurring characters

ValueCountFrequency (%)
0 6993
69.9%
1 3007
30.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6993
69.9%
1 3007
30.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6993
69.9%
1 3007
30.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6993
69.9%
1 3007
30.1%

Interactions

2025-09-17T15:09:20.391683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:11.553975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:12.737174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:13.947106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:15.438080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:16.755275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:18.019120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:19.149376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:20.539278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:11.693116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:12.881330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:14.091444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:15.596622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:16.923654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:18.153066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:19.293744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:20.698715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:11.849896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:13.026345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:14.237730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:15.752836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:17.078038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:18.289424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:19.446730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:20.854253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:11.993001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:13.168057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:14.370621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:15.894631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:17.221939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:18.421182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:19.589406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:21.289331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:12.150115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:13.332170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:14.534085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:16.064187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:17.388039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:18.570279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:19.755878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:21.444092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:12.302297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:13.484749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:14.686730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:16.248661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:17.561967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:18.716243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:19.917460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:21.588581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:12.436462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:13.635129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:14.830507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:16.396278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:17.701483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:18.846238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:20.082761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:21.741101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:12.584755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:13.794970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:14.976021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:16.577469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:17.862477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:18.993843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-17T15:09:20.228702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2025-09-17T15:09:29.240002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
AgeAlcohol_ConsumptionBMICaffeine_mgCoffee_IntakeCountryGenderHealth_IssuesHeart_RateIDOccupationPhysical_Activity_HoursSleep_HoursSleep_QualitySmokingStress_Level
Age1.0000.0260.008-0.012-0.0130.0000.0000.280-0.002-0.0090.0150.0090.0040.0000.0000.000
Alcohol_Consumption0.0261.0000.0000.0000.0000.0000.0000.0000.0000.0000.0280.0160.0320.0240.0060.000
BMI0.0080.0001.000-0.009-0.0080.0130.0150.212-0.010-0.0100.0130.0030.0090.0070.0370.000
Caffeine_mg-0.0120.000-0.0091.0001.0000.0140.0070.0630.056-0.0040.0000.004-0.1900.1030.0000.109
Coffee_Intake-0.0130.000-0.0081.0001.0000.0160.0160.0630.056-0.0040.0000.003-0.1900.1030.0000.110
Country0.0000.0000.0130.0140.0161.0000.0000.0280.0000.0060.0120.0140.0190.0210.0330.014
Gender0.0000.0000.0150.0070.0160.0001.0000.0000.0000.0250.0110.0060.0000.0000.0060.000
Health_Issues0.2800.0000.2120.0630.0630.0280.0001.0000.0110.0100.0140.0000.4220.4610.0110.565
Heart_Rate-0.0020.000-0.0100.0560.0560.0000.0000.0111.0000.0020.012-0.003-0.0370.0160.0140.018
ID-0.0090.000-0.010-0.004-0.0040.0060.0250.0100.0021.0000.000-0.008-0.0070.0000.0090.000
Occupation0.0150.0280.0130.0000.0000.0120.0110.0140.0120.0001.0000.0100.0000.0130.0000.012
Physical_Activity_Hours0.0090.0160.0030.0040.0030.0140.0060.000-0.003-0.0080.0101.000-0.0110.0000.0280.000
Sleep_Hours0.0040.0320.009-0.190-0.1900.0190.0000.422-0.037-0.0070.000-0.0111.0000.8880.0230.887
Sleep_Quality0.0000.0240.0070.1030.1030.0210.0000.4610.0160.0000.0130.0000.8881.0000.0001.000
Smoking0.0000.0060.0370.0000.0000.0330.0060.0110.0140.0090.0000.0280.0230.0001.0000.000
Stress_Level0.0000.0000.0000.1090.1100.0140.0000.5650.0180.0000.0120.0000.8871.0000.0001.000

Missing values

2025-09-17T15:09:21.992094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-17T15:09:22.352915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDAgeGenderCountryCoffee_IntakeCaffeine_mgSleep_HoursSleep_QualityBMIHeart_RateStress_LevelPhysical_Activity_HoursHealth_IssuesOccupationSmokingAlcohol_Consumption
0140MaleGermany3.5328.17.5Good24.978Low14.5NoneOther00
1233MaleGermany1.094.16.2Good20.067Low11.0NoneService00
2342MaleBrazil5.3503.75.9Fair22.759Medium11.2MildOffice00
3453MaleGermany2.6249.27.3Good24.771Low6.6MildOther00
4532FemaleSpain3.1298.05.3Fair24.176Medium8.5MildStudent01
5632MaleMexico3.4326.46.4Good27.082Low8.8NoneService00
6753MaleFrance2.7252.17.8Good24.358Low1.0MildStudent10
7844FemaleCanada4.5423.55.5Fair15.862Medium0.7MildService11
8929MaleUK1.7162.07.1Good21.760Low2.2NoneService11
91041FemaleSwitzerland4.0383.26.4Good30.469Low11.9MildOffice00
IDAgeGenderCountryCoffee_IntakeCaffeine_mgSleep_HoursSleep_QualityBMIHeart_RateStress_LevelPhysical_Activity_HoursHealth_IssuesOccupationSmokingAlcohol_Consumption
9990999152MaleJapan0.329.75.2Fair25.667Medium3.3ModerateOffice00
9991999237MaleUK2.4225.64.3Poor24.256High1.3MildHealthcare01
9992999342OtherSpain2.8262.06.6Good16.877Low4.3NoneService01
9993999418FemaleItaly0.00.06.2Good25.975Low8.3NoneService00
9994999549FemaleGermany1.6150.15.7Fair25.581Medium12.9MildStudent01
9995999650FemaleJapan2.1199.86.0Fair30.550Medium10.1ModerateHealthcare01
9996999718FemaleUK3.4319.25.8Fair19.171Medium11.6MildService00
9997999826MaleChina1.6153.47.1Good25.166Low13.7NoneStudent11
9998999940FemaleFinland3.4327.17.0Good19.380Low0.1NoneStudent00
99991000042FemaleBrazil2.9277.56.4Good28.172Low9.8NoneStudent10